Color and Color Vision

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INTRODUCTION TO IMAGE PROCESSING AND COMPUTER VISION Knowledge Discovery and Data Mining 2 Contents Preface Overview References Chapter 1. Image Presentation 1.1 Visual Perception 1.2 Color Representation 1.3 Image Capture, Representation and Storage Chapter 2. Statistical Operations 2.1 Gray-level Transformation 2.2 Histogram Equalization 2.3 Multi-image Operations Chapter 3. Spatial Operations and Transformations 3.1 Spatial Dependent Transformation 3.2 Templates and Convolutions 3.3 Other Window Operations 3.4 Two-dimensional geometric transformations Chapter 4. Segmentation and Edge Detection 4.1 Region Operations 4.2 Basic Edge detection 4.3 Second-order Detection 4.4 Pyramid Edge Detection 4.5 Crack Edge Relaxation 4.6 Edge Following Chapter 5. Morphological and Other Area Operations 5.1 Morphological Defined 5.2 Basic Morphological Operations 5.3 Opening and Closing Operators Chapter 6. Finding Basic Shapes 6.1 Combining Edges 6.2 Hough Transform Knowledge Discovery and Data Mining 3 6.3 Bresenham’s Algorithms 6.4 Using Interest points 6.5 Problems 6.6 Exercies Chapter 7. Reasoning, Facts and Inferences 7.1 Introduction 7.2 Fact and Rules 7.3 Strategic Learning 7.4 Networks and Spatial Descriptors 7.5 Rule Orders 7.6 Exercises Chapter 8. Object Recognition 8.1 Introduction 8.2 System Component 8.3 Complexity of Object Recognition 8.4 Object Representation 8.5 Feature Detection 8.6 Recognition Strategy 8.7 Verification 8.8 Exercises Chapter 9. The Frequency Domain 9.1 Introduction 9.2 Discrete Fourier Transform 9.3 Fast Fourier Transform 9.4 Filtering in the Frequency Domain 9.5 Discrete Cosine Transform Chapter 10. Image Compression 10.1 Introduction to Image Compression 10.2 Run Length Encoding 10.3 Huffman Coding 10.4 Modified Huffman Coding 10.5 Modified READ 10.6 LZW 10.7 Arithmetic Coding 10.8 JPEG 10.9 Other state-of-the-art Image Compression Methods 10.10 Exercise Knowledge Discovery and Data Mining 4 Preface The field of Image Processing and Computer Vision has been growing at a fast pace. The growth in this field has been both in breadth and depth of concepts and techniques. Computer Vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing, and nanotechnology to multimedia databases. This course aims at providing fundamental techniques of Image Processing and Computer Vision. The text is intended to provide the details to allow vision algorithms to be used in practical applications. As in most developing field, not all aspects of Image Processing and Computer Vision are useful to the designers of a vision system for a specific application. A designer needs to know basic concept and techniques to be successful in designing or evaluating a vision system for a particular application. The text is intended to be used in an introductory course in Image Processing and Computer Vision at the undergraduate or early graduate level and should be suitable for students or any one who uses computer imaging with no priori knowledge of computer graphics or signal processing. But they should have a working knowledge of mathematics, statistical Color and Color Vision Color and Color Vision Bởi: OpenStaxCollege The gift of vision is made richer by the existence of color Objects and lights abound with thousands of hues that stimulate our eyes, brains, and emotions Two basic questions are addressed in this brief treatment—what does color mean in scientific terms, and how we, as humans, perceive it? Simple Theory of Color Vision We have already noted that color is associated with the wavelength of visible electromagnetic radiation When our eyes receive pure-wavelength light, we tend to see only a few colors Six of these (most often listed) are red, orange, yellow, green, blue, and violet These are the rainbow of colors produced when white light is dispersed according to different wavelengths There are thousands of other hues that we can perceive These include brown, teal, gold, pink, and white One simple theory of color vision implies that all these hues are our eye’s response to different combinations of wavelengths This is true to an extent, but we find that color perception is even subtler than our eye’s response for various wavelengths of light The two major types of light-sensing cells (photoreceptors) in the retina are rods and cones Rods are more sensitive than cones by a factor of about 1000 and are solely responsible for peripheral vision as well as vision in very dark environments They are also important for motion detection There are about 120 million rods in the human retina Rods not yield color information You may notice that you lose color vision when it is very dark, but you retain the ability to discern grey scales Take-Home Experiment: Rods and Cones Go into a darkened room from a brightly lit room, or from outside in the Sun How long did it take to start seeing shapes more clearly? What about color? Return to the bright room Did it take a few minutes before you could see things clearly? Demonstrate the sensitivity of foveal vision Look at the letter G in the word ROGERS What about the clarity of the letters on either side of G? Cones are most concentrated in the fovea, the central region of the retina There are no rods here The fovea is at the center of the macula, a mm diameter region responsible 1/6 Color and Color Vision for our central vision The cones work best in bright light and are responsible for high resolution vision There are about million cones in the human retina There are three types of cones, and each type is sensitive to different ranges of wavelengths, as illustrated in [link] A simplified theory of color vision is that there are three primary colors corresponding to the three types of cones The thousands of other hues that we can distinguish among are created by various combinations of stimulations of the three types of cones Color television uses a three-color system in which the screen is covered with equal numbers of red, green, and blue phosphor dots The broad range of hues a viewer sees is produced by various combinations of these three colors For example, you will perceive yellow when red and green are illuminated with the correct ratio of intensities White may be sensed when all three are illuminated Then, it would seem that all hues can be produced by adding three primary colors in various proportions But there is an indication that color vision is more sophisticated There is no unique set of three primary colors Another set that works is yellow, green, and blue A further indication of the need for a more complex theory of color vision is that various different combinations can produce the same hue Yellow can be sensed with yellow light, or with a combination of red and green, and also with white light from which violet has been removed The three-primary-colors aspect of color vision is well established; more sophisticated theories expand on it rather than deny it The image shows the relative sensitivity of the three types of cones, which are named according to wavelengths of greatest sensitivity Rods are about 1000 times more sensitive, and their curve peaks at about 500 nm Evidence for the three types of cones comes from direct measurements in animal and human eyes and testing of color blind people Consider why various objects display color—that is, why are feathers blue and red in a crimson rosella? The true color of an object is defined by its absorptive or reflective characteristics [link] shows white light falling on three different objects, one pure blue, one pure red, and one black, as well as pure red light falling on a white object Other hues are created by more complex absorption characteristics Pink, for example on a galah cockatoo, can be due to weak absorption of all colors except red An object can appear a different color under non-white illumination For example, a pure blue object illuminated with pure red light will appear black, because it absorbs all the red 2/6 Color and Color Vision light falling on it But, the true color of the object is blue, which is independent of illumination ...[...]... (“Display of image is done.\n“); cvWaitKey(0); // wait for a key Now perform the thresholding operation But this is a color image, so convert it to grey first using the average of the three color components for (i=0; i height; i++) for (j=0; j width; j++) { k=( (image- >imageData+i *image- >widthStep)[j *image- >nChannels+0] + (image- >imageData+i *image- >widthStep)[j *image- >nChannels+1] + (image- >imageData+i *image- >widthStep)[j *image- >nChannels+2])/3;... [j * image- >nChannels + 0]; if (k < mean) k = 0; else k = 255; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+0] = (UCHAR) k; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+1] = (UCHAR) k; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+2] = (UCHAR) k; } One final window is created, and the final thresholded image is displayed and saved cvNamedWindow( “thresh“); cvShowImage(... window and display the grey image in it cvNamedWindow( “grey“, CV_WINDOW_AUTOSIZE); cvShowImage( “grey“, image ); cvWaitKey(0); // wait for a key Finally, compute the mean level for use as a threshold and pass through the image again, setting pixels less than the mean to 0 and those greater to 255; mean = mean/count; for (i=0; i height; i++) for (j=0; j width; j++) { k= (image- >imageData+i *image- >widthStep)... + (image- >imageData+i *image- >widthStep)[j *image- >nChannels+1] + (image- >imageData+i *image- >widthStep)[j *image- >nChannels+2])/3; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+0] = (UCHAR) k; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+1] = (UCHAR) k; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+2] = (UCHAR) k; Chapter 1 ■ Practical Aspects of a Vision System At this point in the loop, count and sum the pixel values so... Finding Images by Example 395 Chapter 11 High-Performance Computing for Vision and Image Processing 425 Index 465 xi Contents Preface Chapter 1 Chapter 2 xxi Practical Aspects of a Vision System — Image Display, Input/Output, and Library Calls OpenCV The Basic OpenCV Code The IplImage Data Structure Reading and Writing Images Image Display An Example Image Capture Interfacing with the AIPCV Library... window Before anyone can modify this code in a knowledgeable way, the data structures and functions need to be explained 1.2.1 The IplImage Data Structure The IplImage structure is the in-memory data organization for an image Images in IplImage form can be converted into arrays of pixels, but IplImage also contains a lot of structural information about the image data, which can have many forms For example,... to as indirect access in OpenCV documentation and is slower than other means of accessing pixels It is, on the other hand, clean and clear 1.2.2 Reading and Writing Images The basic function for image input has already been seen; cvLoadImage reads an image from a file, given a path name to that file It can read images in JPEG, BMP, PNM, PNG, and TIF formats, and does so automatically, without the need... argv[]) { IplImage *image = 0; int i,j,k; int mean=0, count=0; char c; image = cvLoadImage(“C:/AIPCV/marchA062.jpg“); At this point, there should be image data [...]... Table 3.4; and as Figures 3.12, 3.13, 3.25À3.28: Davies (2000f) Charles and Davies (2004) Springer-Verlag For permission to reprint portions of the following papers as text in Chapters 6, 21; and as Figures 6.2, 6.4: Davies (1988d), Figs 1À3 Davies, E.R (2003) Design of object location algorithms and their use for food and cereals inspection Chapter 15 in Graves, M and Batchelor, B.G (eds.) Machine Vision.. . 22.16À22.18; 23.1, 23.3, 23.4; 25.4À25.8: Davies (1985; 1988a; 1997b; 1999f; 2000b,c; 2005; 2008b) Davies, E.R (1997) Algorithms for inspection: constraints, tradeoffs and the design process IEE Digest no 1997/041, Colloquium on Industrial Inspection, IEE (10 Feb.), pp 6/1À5 Sugrue and Davies (2007) Mastorakis and Davies (2011) Davies et al (1998a) Davies and Johnstone (1989) IFS Publications Ltd For... published more than 200 papers and three books Machine Vision: Theory, Algorithms, Practicalities (1990), Electronics, Noise and Signal Recovery (1993), and Image Processing for the Food Industry (2000); the first of these has been widely used internationally for more than 20 years, and is now out in this much enhanced fourth edition Roy is a Fellow of the IoP and the IET, and a Senior Member of the IEEE... American Standard Code for Information Interchange application-specific integrated circuit automated teller machine area under curve audio video interleave between-class variance method beta [distribution] sampling consensus British Machine Vision Association block of RAM bidirectional reflectance distribution function computed-aided design computer- aided manufacture charge-coupled device closed-circuit... the quality, and often quantity, of outstanding contributions to the field published in key conferences and journals such as ICCV and PAMI These advances are most clearly reflected by the growing importance of the application areas in which the novel and real-time developments in computer vision have been applied to or developed for Twenty-five years ago, industrial quality inspection and simple military... PH433/96/2, 5 Glossary of Acronyms and Abbreviations 1-D 2-D 3-D 3DPO ACM ADAS ADC AI ANN APF ASCII ASIC ATM AUC AVI BCVM BetaSAC BMVA BRAM BRDF CAD CAM CCD CCTV CDF CIM CLIP CPU DCSM DET DEXA DG DN DoF DoG DSP EM EURASIP FAST FFT one dimension/one-dimensional two dimensions/two-dimensional three dimensions/three-dimensional 3-D part orientation system Association for Computing Machinery (USA) advanced driver... examples and algorithms I am sure that this volume will be welcomed by a great many students Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008, Article ID 487618, 9 pages doi:10.1155/2008/487618 Research Article Quantification and Standardized Description of Color Vision Deficiency Caused by Anomalous Trichromats—Part I: Simulation and Measurement Seungji Yang, 1 Yong Man Ro, 1 Edward K. Wong, 2 and Jin-Hak Lee 3 1 Image and Video Systems Lab, Information and Communications University, Munji 119, Yuseong, Daejeon 305-732, South Korea 2 Department of Ophthalmology, University of California at Irvine, Irvine, CA 92697-4375, USA 3 Department of Ophthalmology, Seoul National University Hospital, 28 Yongon-Dong, Chongno-Gu, Seoul 110-744, South Korea Correspondence should be addressed to Yong Man Ro, yro@icu.ac.kr Received 8 October 2007; Revised 14 December 2007; Accepted 22 December 2007 Recommended by Alain Tremeau The MPEG-21 Multimedia Framework allows visually impaired users to have an improved access to visual content by enabling content adaptation techniques such as color compensation. However, one important issue is the method to create and interpret the standardized CVD descriptions when making the use of generic color vision tests. In Part I of our study to tackle the issue, we present a novel computerized hue test (CHT) to examine and quantify CVD, which allows reproducing and manipulating test colors for the purposes of computer simulation and analysis of CVD. Both objective evaluation via color difference measurement and subjective evaluation via clinical experiment showed that the CHT works well as a color vision test: it is highly correlated with the Farnsworth-Munsell 100 Hue (FM100H) test and allows for a more elaborate and correct color reproduction than the FM100H test. Copyright © 2008 Seungji Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. INTRODUCTION Today, the use of color display in the multimedia-enabled devices is very common. Portable multimedia devices even allow real-time displays of high-quality color information. As such, with the proliferation of color displays, it becomes more important for ordinary people to perceive colors cor- rectly. Color displays create no problems for normal people, although color perception might be slightly different among different people with a different normal color vision. Mean- while, color displays do cause problems for people with color vision deficiency (CVD). For these people, the use of rich col- ors may lead to confusion. It may even result in misinterpret- ing information that colors are carrying because people with CVD may suffer from inability to discriminate among differ- ent colors. The problem even gets worse in cases where color is the only visual clue to recognize something important. Up to 8% of the world’s male population exhibits a type of CVD. More than 80% of them has one form of anoma- lous trichromacy, which demonstrates a milder and variable severity than those with dichromacy [1]. It is known that there is no satisfactory cure for CVD and this condition is lifelong. However, there has been a little consideration about any assistance to people with CVD in color perception. Recently, universal multimedia access (UMA) has be- come an emerging trend in the world of multimedia commu- nication. A UMA system adapts rich multimedia content Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008, Article ID 487618, 9 pages doi:10.1155/2008/487618 Research Article Quantification and Standardized Description of Color Vision Deficiency Caused by Anomalous Trichromats—Part I: Simulation and Measurement Seungji Yang, 1 Yong Man Ro, 1 Edward K. Wong, 2 and Jin-Hak Lee 3 1 Image and Video Systems Lab, Information and Communications University, Munji 119, Yuseong, Daejeon 305-732, South Korea 2 Department of Ophthalmology, University of California at Irvine, Irvine, CA 92697-4375, USA 3 Department of Ophthalmology, Seoul National University Hospital, 28 Yongon-Dong, Chongno-Gu, Seoul 110-744, South Korea Correspondence should be addressed to Yong Man Ro, yro@icu.ac.kr Received 8 October 2007; Revised 14 December 2007; Accepted 22 December 2007 Recommended by Alain Tremeau The MPEG-21 Multimedia Framework allows visually impaired users to have an improved access to visual content by enabling content adaptation techniques such as color compensation. However, one important issue is the method to create and interpret the standardized CVD descriptions when making the use of generic color vision tests. In Part I of our study to tackle the issue, we present a novel computerized hue test (CHT) to examine and quantify CVD, which allows reproducing and manipulating test colors for the purposes of computer simulation and analysis of CVD. Both objective evaluation via color difference measurement and subjective evaluation via clinical experiment showed that the CHT works well as a color vision test: it is highly correlated with the Farnsworth-Munsell 100 Hue (FM100H) test and allows for a more elaborate and correct color reproduction than the FM100H test. Copyright © 2008 Seungji Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. INTRODUCTION Today, the use of color display in the multimedia-enabled devices is very common. Portable multimedia devices even allow real-time displays of high-quality color information. As such, with the proliferation of color displays, it becomes more important for ordinary people to perceive colors cor- rectly. Color displays create no problems for normal people, although color perception might be slightly different among different people with a different normal color vision. Mean- while, color displays do cause problems for people with color vision deficiency (CVD). For these people, the use of rich col- ors may lead to confusion. It may even result in misinterpret- ing information that colors are carrying because people with CVD may suffer from inability to discriminate among differ- ent colors. The problem even gets worse in cases where color is the only visual clue to recognize something important. Up to 8% of the world’s male population exhibits a type of CVD. More than 80% of them has one form of anoma- lous trichromacy, which demonstrates a milder and variable severity than those with dichromacy [1]. It is known that there is no satisfactory cure for CVD and this condition is lifelong. However, there has been a little consideration about any assistance to people with CVD in color perception. Recently, universal multimedia access (UMA) has be- come an emerging trend in the world of multimedia commu- nication. A UMA system adapts rich multimedia content to various constraints imposed by users, devices, and ... Color and Color Vision Color Vision Section Summary • The eye has four types of light receptors—rods and three types of colorsensitive cones • The rods are good for night vision, peripheral vision, ... different colors of the material and hold them up to a white light Using the theory described above, explain the colors you observe You could also try mixing different crayon colors 3/6 Color and Color. .. original scene This implies that color vision can be induced by comparison of the black -and- white and red images Color vision is not completely understood or explained, and the retinex theory is not